A deep network approach to multitemporal cloud detection
This work addresses the problem of accurate, continuous cloud detection for satellite imagery, which is crucial for meteorological and climate monitoring applications.
This paper presents a deep learning model with temporal memory for detecting clouds in image time series from the Seviri imager on the Meteosat Second Generation satellite. The model generates pixel-level cloud maps with confidence, effectively outlining clouds throughout the year, day and night, with high accuracy.
We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation (MSG) satellite. The model provides pixel-level cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.